Since Stratio’s creation in 2014, we have posted a total of 86 posts on our blog. We would like to congratulate and thank all those Stratians who have written their posts and taught us about their specialities and discoveries in relation to Spark, Machine Learning, Deep Learning, Scala, business, Kafka… We know that is hard to find time to read all of the blog posts, so here you have a recap of the 3 most-read posts published on our blog!
Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization! In part 1, we talked about a family of metaheuristic algorithms known generically as Ant Colony Optimization (ACO), which were specially well-suited for combinatorial optimization problems, i.e. finding the best combination of values of many categorical variables. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
“A chain is only as strong as its weakest link” – English proverb
There is one striking element that does not seem to have been addressed as a common purpose between business teams and IT teams when confronting Innovation or Digital Transformation roadmaps… A Data Management Strategy.
In the previous post about Apache Ignite, we learnt how to set up and create either a simple cache or a sql cache, and share the cached data between different nodes. In this post, we will dig a little deeper. We will see what to do if our app crashes because the cached data has disappeared. How could Ignite help us avoid this problem?
Did you know that the word “hippopotamus” is a word of Greek origin? Hippos- comes from “horse” and -potamos means “river”. The funny thing here would be to imagine when Greeks run into this animal for the very first time. There was not a word for every single animal around the world, so they probably thought something like “what a strange horse…!!! Maybe the river has something to do with it. Got it! It will be a hippo-potamus!”
This is the second post of our Wild Data series. In this post, we are going to expose how to transfer style from one image to another. Here, the most interesting point is to know that we won’t use a neural network to classify a set of classes as usual. I mean, we don’t need to train a network for a specific approach. Transfer style is based on pre-trained networks such as it could be a VGG19 trained with ImageNet (one million of images). Thus, a good understanding of transfer style will help you to better understand how convolutional neural networks works for vision. Let’s go on!
Let’s imagine that you want to buy a new car, and you fall in love with this new car’s brand. Because you really want that car, the car’s brand comes out everywhere in your daily life, even though the amount of these cars remain the same. Our brain is trained to focus on what it wants to see.
In a previous post, we reviewed the taxonomy of metaheuristic algorithms for optimization within the context of feature selection in machine learning problems. We explained how feature selection can be tackled as a combinatorial optimization problem in a huge search space, and how heuristic algorithms (or simply metaheuristics) are able to find good solutions -although not necessarily optimal- in a reasonable amount of time by exploring such space in an intelligent manner. Recall that metaheuristics are especially well fitted when the function being optimized is non-differentiable or does not have an analytical expression at all (for instance, the magnitude being optimized is the result of a randomized complex simulation under a parameter set that constitutes a candidate solution). Note that maths cannot help us in such cases and metaheuristics can be the only way to go.
This post aims to show how to build an on-premise Mesos architecture to handle a disaster scenario when an entire Data Center is not available, covering also some framework strategies for zero data loss.
When the Father of Statistics Ronald Fisher started to witness mounting evidence in favor of the association between smoking and lung cancer, he was quick to fall back into the maxim he helped coin “Correlation does not imply causation”, discounting the evidence as spurious and continuing his habit as a heavy smoker of cigarettes. While his command of mathematics was well above his contemporaries at the time, this speaks volumes for the fact that everybody is prone to bias, and that we should attempt not to fall in love with our hypotheses too early in our decision making process. While Fisher may have had a point, the fact that he was a smoker himself certainly clouded his thinking and led him to not consider fairly all possible explanations for the available evidence.